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Predicting microseismic, acoustic emission and electromagnetic radiation data using neural networks

Microseism, acoustic emission and electromagnetic radiation (M-A-E) data are usually used for predicting rockburst hazards. However, it is a great challenge to realize the prediction of M-A-E data. In this study, with the aid of a deep learning algorithm, a new method for the prediction of M-A-E dat...

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Bibliografische Detailangaben
Hauptverfasser: Yangyang Di, Enyuan Wang, Zhonghui Li, Xiaofei Liu, Tao Huang, Jiajie Yao
Format: Artigo
Sprache:Inglês
Veröffentlicht: Elsevier 2024-02-01
Schriftenreihe:Journal of Rock Mechanics and Geotechnical Engineering
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Online-Zugang:http://www.sciencedirect.com/science/article/pii/S1674775523001907
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